Added Infer file

.gitignore

@@ -12,7 +12,8 @@ data/.DS_Store
 *.gz
 .spyproject/
 .vscode/*
-model.npz
 env/
 venv/
 .idea/
+test.py
+chat.txt

data/reddit_data/data.py

@@ -0,0 +1,288 @@
+EN_WHITELIST = '0123456789abcdefghijklmnopqrstuvwxyz ' # space is included in whitelist
+EN_BLACKLIST = '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~\''
+
+FILENAME = 'chat.txt' 
+
+limit = {
+        'maxq' : 20,
+        'minq' : 0,
+        'maxa' : 20,
+        'mina' : 3
+        }
+
+UNK = 'unk'
+VOCAB_SIZE = 6000
+
+import random
+import sys
+
+import nltk
+import itertools
+from collections import defaultdict
+
+import numpy as np
+
+import pickle
+
+
+def ddefault():
+    return 1
+
+'''
+ read lines from file
+     return [list of lines]
+'''
+
+def read_lines(filename):
+    return open(filename).read().split('\n')[:-1]
+
+
+'''
+ split sentences in one line
+  into multiple lines
+    return [list of lines]
+
+'''
+def split_line(line):
+    return line.split('.')
+
+
+'''
+ remove anything that isn't in the vocabulary
+    return str(pure ta/en)
+
+'''
+def filter_line(line, whitelist):
+    return ''.join([ ch for ch in line if ch in whitelist ])
+
+
+'''
+ read list of words, create index to word,
+  word to index dictionaries
+    return tuple( vocab->(word, count), idx2w, w2idx )
+
+'''
+def index_(tokenized_sentences, vocab_size):
+    # get frequency distribution
+    freq_dist = nltk.FreqDist(itertools.chain(*tokenized_sentences))
+    # get vocabulary of 'vocab_size' most used words
+    vocab = freq_dist.most_common(vocab_size)
+    # index2word
+    index2word = ['_'] + [UNK] + [ x[0] for x in vocab ]
+    # word2index
+    word2index = dict([(w,i) for i,w in enumerate(index2word)] )
+    return index2word, word2index, freq_dist
+
+
+'''
+ filter too long and too short sequences
+    return tuple( filtered_ta, filtered_en )
+
+'''
+def filter_data(sequences):
+    filtered_q, filtered_a = [], []
+    raw_data_len = len(sequences)//2
+
+    for i in range(0, len(sequences), 2):
+        qlen, alen = len(sequences[i].split(' ')), len(sequences[i+1].split(' '))
+        if qlen >= limit['minq'] and qlen <= limit['maxq']:
+            if alen >= limit['mina'] and alen <= limit['maxa']:
+                filtered_q.append(sequences[i])
+                filtered_a.append(sequences[i+1])
+
+    # print the fraction of the original data, filtered
+    filt_data_len = len(filtered_q)
+    filtered = int((raw_data_len - filt_data_len)*100/raw_data_len)
+    print(str(filtered) + '% filtered from original data')
+
+    return filtered_q, filtered_a
+
+
+
+
+
+'''
+ create the final dataset :
+  - convert list of items to arrays of indices
+  - add zero padding
+      return ( [array_en([indices]), array_ta([indices]) )
+
+'''
+def zero_pad(qtokenized, atokenized, w2idx):
+    # num of rows
+    data_len = len(qtokenized)
+
+    # numpy arrays to store indices
+    idx_q = np.zeros([data_len, limit['maxq']], dtype=np.int32)
+    idx_a = np.zeros([data_len, limit['maxa']], dtype=np.int32)
+
+    for i in range(data_len):
+        q_indices = pad_seq(qtokenized[i], w2idx, limit['maxq'])
+        a_indices = pad_seq(atokenized[i], w2idx, limit['maxa'])
+
+        #print(len(idx_q[i]), len(q_indices))
+        #print(len(idx_a[i]), len(a_indices))
+        idx_q[i] = np.array(q_indices)
+        idx_a[i] = np.array(a_indices)
+
+    return idx_q, idx_a
+
+
+'''
+ replace words with indices in a sequence
+  replace with unknown if word not in lookup
+    return [list of indices]
+
+'''
+def pad_seq(seq, lookup, maxlen):
+    indices = []
+    for word in seq:
+        if word in lookup:
+            indices.append(lookup[word])
+        else:
+            indices.append(lookup[UNK])
+    return indices + [0]*(maxlen - len(seq))
+
+
+def process_data():
+
+    print('\n>> Read lines from file')
+    lines = read_lines(filename=FILENAME)
+
+    # change to lower case (just for en)
+    lines = [ line.lower() for line in lines ]
+
+    print('\n:: Sample from read(p) lines')
+    print(lines[121:125])
+
+    # filter out unnecessary characters
+    print('\n>> Filter lines')
+    lines = [ filter_line(line, EN_WHITELIST) for line in lines ]
+    print(lines[121:125])
+
+    # filter out too long or too short sequences
+    print('\n>> 2nd layer of filtering')
+    qlines, alines = filter_data(lines)
+    print('\nq : {0} ; a : {1}'.format(qlines[60], alines[60]))
+    print('\nq : {0} ; a : {1}'.format(qlines[61], alines[61]))
+
+
+    # convert list of [lines of text] into list of [list of words ]
+    print('\n>> Segment lines into words')
+    qtokenized = [ wordlist.split(' ') for wordlist in qlines ]
+    atokenized = [ wordlist.split(' ') for wordlist in alines ]
+    print('\n:: Sample from segmented list of words')
+    print('\nq : {0} ; a : {1}'.format(qtokenized[60], atokenized[60]))
+    print('\nq : {0} ; a : {1}'.format(qtokenized[61], atokenized[61]))
+
+
+    # indexing -> idx2w, w2idx : en/ta
+    print('\n >> Index words')
+    idx2w, w2idx, freq_dist = index_( qtokenized + atokenized, vocab_size=VOCAB_SIZE)
+
+    print('\n >> Zero Padding')
+    idx_q, idx_a = zero_pad(qtokenized, atokenized, w2idx)
+
+    print('\n >> Save numpy arrays to disk')
+    # save them
+    np.save('idx_q.npy', idx_q)
+    np.save('idx_a.npy', idx_a)
+
+    # let us now save the necessary dictionaries
+    metadata = {
+            'w2idx' : w2idx,
+            'idx2w' : idx2w,
+            'limit' : limit,
+            'freq_dist' : freq_dist
+                }
+
+    # write to disk : data control dictionaries
+    with open('metadata.pkl', 'wb') as f:
+        pickle.dump(metadata, f)
+
+def load_data(PATH=''):
+    # read data control dictionaries
+    try:
+        with open(PATH + 'metadata.pkl', 'rb') as f:
+            metadata = pickle.load(f)
+    except:
+        metadata = None
+    # read numpy arrays
+    idx_q = np.load(PATH + 'idx_q.npy')
+    idx_a = np.load(PATH + 'idx_a.npy')
+    return metadata, idx_q, idx_a
+
+import numpy as np
+from random import sample
+
+'''
+ split data into train (70%), test (15%) and valid(15%)
+    return tuple( (trainX, trainY), (testX,testY), (validX,validY) )
+
+'''
+def split_dataset(x, y, ratio = [0.7, 0.15, 0.15] ):
+    # number of examples
+    data_len = len(x)
+    lens = [ int(data_len*item) for item in ratio ]
+
+    trainX, trainY = x[:lens[0]], y[:lens[0]]
+    testX, testY = x[lens[0]:lens[0]+lens[1]], y[lens[0]:lens[0]+lens[1]]
+    validX, validY = x[-lens[-1]:], y[-lens[-1]:]
+
+    return (trainX,trainY), (testX,testY), (validX,validY)
+
+
+'''
+ generate batches from dataset
+    yield (x_gen, y_gen)
+
+    TODO : fix needed
+
+'''
+def batch_gen(x, y, batch_size):
+    # infinite while
+    while True:
+        for i in range(0, len(x), batch_size):
+            if (i+1)*batch_size < len(x):
+                yield x[i : (i+1)*batch_size ].T, y[i : (i+1)*batch_size ].T
+
+'''
+ generate batches, by random sampling a bunch of items
+    yield (x_gen, y_gen)
+
+'''
+def rand_batch_gen(x, y, batch_size):
+    while True:
+        sample_idx = sample(list(np.arange(len(x))), batch_size)
+        yield x[sample_idx].T, y[sample_idx].T
+
+#'''
+# convert indices of alphabets into a string (word)
+#    return str(word)
+#
+#'''
+#def decode_word(alpha_seq, idx2alpha):
+#    return ''.join([ idx2alpha[alpha] for alpha in alpha_seq if alpha ])
+#
+#
+#'''
+# convert indices of phonemes into list of phonemes (as string)
+#    return str(phoneme_list)
+#
+#'''
+#def decode_phonemes(pho_seq, idx2pho):
+#    return ' '.join( [ idx2pho[pho] for pho in pho_seq if pho ])
+
+
+'''
+ a generic decode function
+    inputs : sequence, lookup
+
+'''
+def decode(sequence, lookup, separator=''): # 0 used for padding, is ignored
+    return separator.join([ lookup[element] for element in sequence if element ])
+
+
+
+if __name__ == '__main__':
+    process_data()

data/twitter/data.py

@@ -1,7 +1,7 @@
 EN_WHITELIST = '0123456789abcdefghijklmnopqrstuvwxyz ' # space is included in whitelist
 EN_BLACKLIST = '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~\''
 
-FILENAME = 'data/chat.txt'
+FILENAME = 'chat.txt' 
 
 limit = {
         'maxq' : 20,
@@ -31,8 +31,8 @@ def ddefault():
 '''
  read lines from file
      return [list of lines]
-
 '''
+
 def read_lines(filename):
     return open(filename).read().split('\n')[:-1]
 

infer.py

@@ -0,0 +1,21 @@
+import os
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
+from main import * # import the main python file with model from the example
+import time
+import tensorlayer as tl
+
+load_weights = tl.files.load_npz(name='saved/model.npz')
+tl.files.assign_weights(load_weights, model_)
+
+top_n = 3
+
+def respond(input):
+    sentence = inference(input, top_n)
+    response=' '.join(sentence)
+    return response
+
+while True:
+    userInput = input("Query > ")
+    for i in range(top_n):
+        print("bot# ", respond(userInput))
+